Datadog Scout 1.0: Cheaper AI SRE Investigations, Run More Often
Datadog Scout 1.0 is an SRE model Datadog says matches frontier models at 3 to 8 times lower cost. What it changes for on-call teams using Bits AI.
Octopus Deploy Remote MCP: Agents Can Now Ship Releases
Octopus Deploy remote MCP lets AI agents deploy releases and run runbooks through a built-in /mcp endpoint. What it exposes, and the guardrails to set first.
Zero-Trust AI Agent Access in Practice: IBM watsonx and HashiCorp Boundary
A HashiCorp proof of concept gives AI agents infrastructure access via Boundary and Vault without exposing credentials, with IBM watsonx doing the reasoning.
Datadog Trained a 9B Model for $0.003 Per Alert Investigation — Here's the Architecture
Datadog fine-tuned a 9B model to 87% of its teacher's recall at 5% of the cost, about $0.003 per production alert investigation. The architecture behind it.
The Datadog Loop: Five Stages for Turning Agent Traces Into Verified Improvements
Datadog's five-stage optimization loop — Observe, Hypothesize, Evaluate, Experiment, Ship — gives platform teams a repeatable method for improving AI agents without blind prompt guessing.
From Traces to Experiments: The 5-Stage Loop for Improving AI Agents in Production
Datadog's framework for turning AI agent traces into actionable improvements — from pattern identification to production A/B testing — addresses the gap that offline evaluation alone can't close.
What Datadog Learned About AI Agent Security by Monitoring Its Own Agents
Datadog's internal agent monitoring revealed that 69% of input tokens live in system prompts teams can't inspect, coding agents execute repo-controlled code before the first user message, and prompt injection bypasses model evaluation entirely.
Golden Paths for AI Agents: What Platform Engineering Gets Wrong When the User Isn't Human
Datadog's framework for redesigning golden paths when AI agents replace human developers as platform users — deterministic enforcement, machine-readable contracts, and audit co-authorship.
Golden Paths for AI Agents: Redesigning Platform Workflows for Non-Human Users
Agents are now platform users. Datadog maps three required changes: execution patterns, deterministic controls, and dispatch authorization before agent runs.
Agent Immunization: The Software Supply Chain Security Model Built for AI Speed
JFrog's agent immunization framework embeds trust enforcement inside what AI agents consume—not at the perimeter—because perimeter security cannot keep pace with agentic development velocity.
How Bedrock AgentCore Propagates User Identity to JFrog Artifactory — and Why It Matters
When an AI agent calls a tool on behalf of a user, who gets the audit trail? Bedrock AgentCore's On-Behalf-Of token exchange solves the shared-credential problem that makes agent-to-Artifactory calls untrustworthy.
Datadog Risk AI: How Systemic Risk Detection Moves Beyond Individual Findings
Datadog's Risk Engineering team built an AI agent layer that correlates security signals across vulnerability, identity, and exposure dimensions — recognizing that severity and risk aren't the same thing.
Datadog Work Management: One Queue for Humans and AI Agents
Datadog's renamed Work Management platform centralizes human and AI agent task tracking with full observability context attached. Here's what it solves that Jira and Slack threads don't.
The Practical Guide to AI Sandboxes for Developers
AI coding agents have deleted home directories and a production database. What an AI sandbox is, how isolation levels differ, and where it fits.
Datadog's Mambark: A 97M-Parameter Filter That Cuts AI Security Costs 10,000x
Datadog's Mambark, a 96.9M-parameter state-space model, screens 10 billion daily security events down to ~10,000 candidates before an AI agent ever runs.
Datadog's MCP Security Agents: What Production Looks Like Beyond the Demo
Datadog built MCP tools for Cloud SIEM operations and published the engineering decisions. Progressive disclosure cut token usage 41%. Here's the architecture.
Datadog: What a Sixth Gartner Lead Means for AI-Driven Ops Teams
For the sixth straight year, Datadog tops Gartner's Observability MQ. Here's what the recognition reveals about observability in an AI-native world.
Datadog Agent Observability Brings AI App Debugging Into Your Coding Agent
Teams spend 60–80% of AI development time on evaluation and error analysis. Datadog's new Agent Observability workflow surfaces production trace data directly into coding agents to cut that cycle.
Datadog Acquires Adaptive ML: Production-Trained AI Agents for Observability
Datadog acquired Adaptive ML to embed specialized model training into its observability platform — shifting AI agents from generic APIs to systems trained on real production signals.
Why Uniform AI Agent Governance Fails — and What to Do Instead
Gartner projects 40% of enterprises will demote or decommission AI agents by 2027 due to governance failures. JFrog's analysis of why one-size-fits-all governance breaks down, and the tiered alternative.
JFrog Brings Artifact Governance to Coding Agent Plugins
JFrog Agent Plugins Repositories treats coding agent plugins as governed software artifacts in Artifactory — closing the supply chain gap where Claude Code, Cursor, and Codex plugins arrive from uncontrolled GitHub repos and Slack channels.
Patterns of Validation: How to Keep AI Agents from Wrecking Your Codebase
CircleCI's layered validation framework — feedforward instructions, mechanical hooks, and post-deploy observability — addresses the core ways AI coding agents fail silently.
AI Agent Guardrails: Where You Place Them Matters as Much as What They Do
Reactive guardrails on tool outputs miss the attack. Why pre-execution placement and multi-hook coverage change AI agent security posture.